{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>点赞数</th>\n",
       "      <th>转发数</th>\n",
       "      <th>热度指数</th>\n",
       "      <th>文章评级</th>\n",
       "      <th>浏览量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2646</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>260004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>816</td>\n",
       "      <td>816.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>100004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1224</td>\n",
       "      <td>612.0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>164502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1261</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>163001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1720</td>\n",
       "      <td>1720.0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>260401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    点赞数     转发数  热度指数  文章评级     浏览量\n",
       "0  2646  1347.0     7     5  260004\n",
       "1   816   816.0     4     6  100004\n",
       "2  1224   612.0     6     5  164502\n",
       "3  1261  1261.0     6     6  163001\n",
       "4  1720  1720.0     7     5  260401"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_ads = pd.read_csv('data.csv')\n",
    "df_ads.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 观察散点图，存在线性关系？\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(df_ads['点赞数'], df_ads['浏览量'], 'r.', label='Training data')\n",
    "plt.xlabel('good')\n",
    "plt.ylabel('view')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 9.5, 0, 800000)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 通过箱线图，查看离群点\n",
    "import seaborn as sns\n",
    "\n",
    "data = pd.concat([df_ads['浏览量'], df_ads['热度指数']], axis=1) \n",
    "fig = sns.boxplot(x='热度指数', y='浏览量', data = data) # 用seaborn的箱线图画图\n",
    "fig.axis(ymin=0, ymax=800000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据清洗\n",
    "df_ads.isna().sum()\n",
    "df_ads = df_ads.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    点赞数     转发数  热度指数  文章评级\n",
      "0  2646  1347.0     7     5\n",
      "1   816   816.0     4     6\n",
      "2  1224   612.0     6     5\n",
      "3  1261  1261.0     6     6\n",
      "4  1720  1720.0     7     5\n",
      "0    260004\n",
      "1    100004\n",
      "2    164502\n",
      "3    163001\n",
      "4    260401\n",
      "Name: 浏览量, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 构建特征集和标签集\n",
    "X = df_ads.drop(['浏览量'], axis=1) # 特征集，删除掉标签相关字段\n",
    "y = df_ads.浏览量                  # 标签集，\n",
    "print(X.head())\n",
    "print(y.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       点赞数     转发数  热度指数  文章评级\n",
      "742   2169  1284.0     8     5\n",
      "759   2229   755.0     6     6\n",
      "1249  1792   963.0     7     5\n",
      "1181  2521   942.0     6     4\n",
      "1203  2515  2035.0     6     3\n",
      "742     228501\n",
      "759     104002\n",
      "1249    231504\n",
      "1181    151401\n",
      "1203    200629\n",
      "Name: 浏览量, dtype: int64\n",
      "       点赞数     转发数  热度指数  文章评级\n",
      "150   1768   884.0     7     5\n",
      "36     988   988.0     5     5\n",
      "1049  2062  1157.0     7     5\n",
      "393   1392  1392.0     5     6\n",
      "1255  1537  1319.0     6     9\n",
      "150     224904\n",
      "36      113001\n",
      "1049    277002\n",
      "393     121502\n",
      "1255    174002\n",
      "Name: 浏览量, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 拆分训练集、验证集、测试集\n",
    "# 将数据集进行80%（训练集）和20%（验证集）的分割\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                   test_size=0.2, random_state=10)\n",
    "print(X_train.head())\n",
    "print(y_train.head())\n",
    "print(X_test.head())\n",
    "print(y_test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       点赞数     转发数  热度指数  文章评级   浏览量真值         浏览量预测值\n",
      "150   1768   884.0     7     5  224904  204326.495272\n",
      "36     988   988.0     5     5  113001  117182.827329\n",
      "1049  2062  1157.0     7     5  277002  236266.050505\n",
      "393   1392  1392.0     5     6  121502  166316.290479\n",
      "1255  1537  1319.0     6     9  174002  206254.835807\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 建立模型\n",
    "from sklearn.linear_model import LinearRegression # 导入线性回归算法模型\n",
    "linereg_model = LinearRegression() # 使用线性回归算法创建模型\n",
    "\n",
    "# 训练模型\n",
    "linereg_model.fit(X_train, y_train) # 用训练集数据\n",
    "\n",
    "# 预测测试集的Y值\n",
    "y_pred = linereg_model.predict(X_test) \n",
    "\n",
    "df_ads_pred = X_test.copy()\n",
    "df_ads_pred['浏览量真值'] = y_test\n",
    "df_ads_pred['浏览量预测值'] = y_pred\n",
    "print(df_ads_pred.head())\n",
    "\n",
    "tick = range(0,len(y_test))\n",
    "plt.plot(tick, y_test, label='test')\n",
    "plt.plot(tick, y_pred, label='pred')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前模型4个特征的权重： [   52.4893649     60.46769946 26245.3020327   3498.80914868]\n",
      "当前模型的截距： -143139.3081655697\n"
     ]
    }
   ],
   "source": [
    "print('当前模型4个特征的权重：',linereg_model.coef_)\n",
    "print('当前模型的截距：',linereg_model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "线性回归预测评分: 0.4189365095307418\n"
     ]
    }
   ],
   "source": [
    "print('线性回归预测评分:', linereg_model.score(X_test, y_test))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
